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A Novel Deep Raman Spectroscopy Platform for Non-Invasive In-Vivo Diagnosis of Breast Cancer

A Novel Deep Raman Spectroscopy Platform for Non-Invasive In-Vivo Diagnosis of Breast Cancer
用于乳腺癌非侵入性体内诊断的新型深度拉曼光谱平台
批准号:
EP/P012442/1
负责人:
Nicholas Stone
金额:
$152.78万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2017
资助国家:
英国
项目状态:
已结题
起止时间:
2017 至 --

项目摘要

项目成果

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中文摘要
翻译
最近,我们在激光光谱学领域开创了一系列革命性的光学技术,即深度拉曼光谱,用于生物组织的非侵入性分子探测。这些发展有可能在包括癌症诊断在内的许多医学领域产生重大变化。这些技术包括空间偏移拉曼光谱(SOR)和传输拉曼(均由申请人申请专利)。这些方法在教程评论中进行了详细描述:http://pubs.rsc.org/en/content/articlelanding/2016/cs/c5cs00466g.临床上迫切需要对许多类型的亚表面下癌症进行早期客观诊断和预测可能的治疗结果。现有技术没有解决这一问题。在癌症临床路径上有许多步骤,实时的、活体的、分子特异性的疾病分析将产生重大影响。这将大大减少针吸活检,在那些在乳房X光检查后召回的人中,约80%的人认为这一步骤是不必要的--即导致良性病变的诊断。通过改进筛查或监测技术,我们的新方法将允许在首次出现时结合乳房X光检查进行更准确和即时的诊断,从而实现更早的诊断和更好的治疗结果。其次,它将使手术切缘评估和治疗实时监测,以及第三,在常规手术中识别淋巴系统的转移侵犯。在许多其他领域,在诊所或剧院环境中对组织样本进行快速分子分析将有助于改进临床决策,例如,在手术前对疾病进行分期时,特别是在化疗/放射治疗期间非侵入性监测肿瘤反应时。显然,这些方法将通过降低癌症复发率而对患者有利;还通过最大限度地减少所需的侵入性手术的数量,从而降低成本和患者的焦虑。拉曼光谱是一种高度分子特异性的方法,其本身已被证明在早期上皮癌诊断中是一种有用的工具,尽管在其传统形式下,它被限制在对深度远小于1毫米的组织表面进行采样。这项新技术开启了对深达几厘米的组织异常的独特访问,即深度比以前使用Raman高出一到两个数量级的深度。在我们之前的项目中,我们能够展示出与早期可行性工作相比,信号恢复在概念上提高了约100倍,现在我们能够使用这种方法快速开发一个用于实际临床工具的平台。我们建议在这一领域取得重大突破,并推进诊断,特别是最初作为重点病例研究的乳腺癌和淋巴转移,然后可能应用于前列腺癌(不在本提案的范围内)。这将作为这一领域的两位关键研究人员斯通教授和马图塞克教授之间的联合跨学科研究项目进行探索。我们现在寻求资金,通过开发一个具有重大社会影响的新型医疗诊断平台,及时推进这项工作。我们建议汇聚来自物理科学、光谱学、放射学、癌症诊断和治疗外科以及组织病理学等多学科领域的关键参与者,以利用所有相关技能并开发关键的专业知识来解决这些具有挑战性的问题。
英文摘要
Recently, we have pioneered a portfolio of revolutionary optical technologies in the area of laser spectroscopy, namely deep Raman spectroscopy, for non-invasive molecular probing of biological tissue. The developments have the potential of making a step-change in many fields of medicine including cancer diagnosis. The techniques comprise spatially offset Raman spectroscopy (SORS) and Transmission Raman (both patented by the applicants). The methods are described in detail in a tutorial review: http://pubs.rsc.org/en/content/articlelanding/2016/cs/c5cs00466g. There is an urgent clinical need for early objective diagnosis and prediction of likely treatment outcomes for many types of subsurface cancers. This is not addressed by existing technologies. There are numerous steps along the cancer clinical pathway where real-time, in vivo, molecular specific disease analysis would have a major impact. This would significantly reduce needle biopsy, in around 80% of those recalled following mammographic screening this step is unnecessarily - ie leading to the diagnosis of benign lesions. Our novel approach would allow for more accurate and immediate diagnosis in conjunction with mammography at first presentation by improving screening or surveillance techniques, leading to earlier diagnosis and better treatment outcomes. Secondly it would allow surgical margin assessment and treatment monitoring in real-time and thirdly identification of metastatic invasion in the lymphatic system during routine surgery. There are numerous other areas where a rapid molecular analysis of a tissue sample in the clinic or theatre environment would allow improved clinical decision-making, for example when pre- operatively staging the disease and particularly when non-invasively monitoring tumour response during chemo/radiotherapy. Clearly these approaches would be beneficial to the patient by reducing cancer recurrence rates; but also by minimising the numbers of invasive procedures required, thus reducing costs and patient anxiety.Raman spectroscopy is a highly molecular-specific method, which itself has proven to be a useful tool in early epithelial cancer diagnostics, although in its conventional form it has been restricted to sampling the tissue surface of much less than 1 mm deep. The new technology unlocks unique access to tissue abnormalities of up to several cm's deep, i.e. at depths one to two orders of magnitude higher than those previously possible with Raman.Following on from our previous project, where we were able to demonstrate conceptually a ~100x improvement in signal recovery compared to our early feasibility work, we are now able to rapidly develop a platform for real-clinical tools using this approach. We propose to make major breakthroughs in this area and advance diagnostics particularly focussed on breast cancer and lymph node metastasis initially as focused case studies and then potentially applied to prostate cancers (outside the scope of this proposal). This will be explored as a joint cross-disciplinary research venture between Profs Stone and Matousek, the two key researchers in this area. We now seek funding to progress this work in a timely manner by developing a novel medical diagnostic platform of major societal impact. We propose to bring together key players from multidisciplinary areas covering physical sciences, spectroscopy, radiology, cancer diagnostic and therapeutic surgery, and histopathology to exploit all of the relevant skills and develop a critical mass of expertise to tackle these challenging issues.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
DOI: 10.1021/acs.analchem.7b01469
发表时间: 2017-09-19
期刊: ANALYTICAL CHEMISTRY
影响因子: 7.4
作者: [Gardner, Benjamin, Stone, Nicholas, Matousek, Pavel]
通讯作者: Matousek, Pavel
Guided principal component analysis (GPCA): a simple method for improving detection of a known analyte
引导主成分分析 (GPCA):一种改进已知分析物检测的简单方法
DOI: 10.1039/d3an00820g
发表时间: 2023
期刊: The Analyst
影响因子: --
作者: [Gardner B]
通讯作者: Gardner B
DOI: 10.1038/s41598-018-25465-x
发表时间: 2018-05-30
期刊: Scientific reports
影响因子: 4.6
作者: [Ghita A, Matousek P, Stone N]
通讯作者: Stone N
DOI: 10.1002/jrs.5875
发表时间: 2020-03-20
期刊: JOURNAL OF RAMAN SPECTROSCOPY
影响因子: 2.5
作者: [Gardner, Benjamin, Stone, Nicholas, Matousek, Pavel]
通讯作者: Matousek, Pavel
Raman Nanotheranostics - RaNT - developing the targeted diagnostics and therapeutics of the future by combining light and functionalised nanoparticles
  • 批准号:
    EP/R020965/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $733.0万
  • 财政年份:
    2018
  • 负责人:
    Nicholas Stone
  • 依托单位:
A novel Deep Raman spectroscopy platform for non-invasive in situ molecular analysis of disease specific tissue compositional changes.
  • 批准号:
    EP/K020374/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $92.46万
  • 财政年份:
    2013
  • 负责人:
    Nicholas Stone
  • 依托单位:
国内基金
海外基金
Deep Seek引导下预防肝硬化腹水患者发生腹腔感染的约翰霍普金斯循证实践模型下中医护理策略的构建研究
  • 批准号:
    2026JJ81909
  • 项目类别:
    省市级项目
  • 资助金额:
    --
  • 批准年份:
    2026
  • 负责人:
    胡曦
  • 依托单位:
基于Deep Unrolling的高分辨近红外二区荧光分子断层成像方法研究
  • 批准号:
    12271434
  • 项目类别:
    面上项目
  • 资助金额:
    46万元
  • 批准年份:
    2022
  • 负责人:
    贺小伟
  • 依托单位:
基于深度森林(Deep Forest)模型的表面增强拉曼光谱分析方法研究
  • 批准号:
    2020A151501709
  • 项目类别:
    省市级项目
  • 资助金额:
    10.0万元
  • 批准年份:
    2020
  • 负责人:
    谢怡
  • 依托单位:
面向Deep Web的数据整合关键技术研究
  • 批准号:
    61872168
  • 项目类别:
    面上项目
  • 资助金额:
    62.0万元
  • 批准年份:
    2018
  • 负责人:
    董永权
  • 依托单位: